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name: learning-and-baselines description: "Use ManiSkill RL/IL baseline families, benchmark task sets, evaluation conventions, and data-generation helpers without launching long training runs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Learning and Baselines

Use this sub-skill when the task is to understand, compare, or prepare ManiSkill reinforcement-learning and imitation-learning baselines.

Route here for

• PPO, SAC, TD-MPC2, BC, ACT, Diffusion Policy, RFCL, and RLPD
• baseline-specific setup, dependency, logging, checkpoint, and evaluation questions
• standard benchmark task-set selection and fair comparison conventions
• benchmark data-generation helpers used to create or replay datasets
• common pitfalls around wandb, missing extras, backend mismatch, dataset mismatch, and long-running workflows

Do not handle here

• gym.make, observation/control/render mode setup, wrapper semantics, or CPU/GPU simulation selection -> environment-usage
• trajectory download/replay/conversion, teleoperation, or motion-planning details -> trajectories-and-datasets
• custom task or benchmark-task authoring -> custom-environments

How to work

1. Identify the family: online RL, supervised imitation learning, online learning from demonstrations, or model-based RL.
2. Consult the bundled references before recommending commands or evaluation settings.
3. Keep baseline scripts and data-generation helpers reference-only unless the user explicitly asks to run them.
4. Match the evaluation protocol to the benchmark family: no partial resets, reconfigure on reset, and record standard metrics.
5. If the request crosses into trajectory replay, dataset conversion, or demo file-layout details, route that part away.

Key rules

• Treat baseline training and benchmark sweeps as expensive by default.
• Do not assume wandb login, external clones, or dataset availability.
• Keep data-collection backend and training/evaluation backend aligned when fairness depends on it.
• Prefer exact command families and relevant flags rather than inventing a new launcher.
• Follow the current docs when old comments or older examples disagree; note the mismatch instead of guessing.

Bundled references

• references/baselines.md
• references/benchmark-tasks.md
• references/evaluation.md
• references/troubleshooting.md
• references/data-generation.md

Bundled script

• scripts/baseline_surface_index.py — prints a safe index of the baseline families, benchmark sets, evaluation contract, and data-generation helpers; never starts training.

Start with references/baselines.md for algorithm coverage and references/evaluation.md for the fair-evaluation contract.